Data Engineer

About Winston Artory Group

WAG is transforming how art and collectibles are valued, managed, and traded. Born from the merger of Winston Art Group (the largest independent appraisal and advisory firm in the U.S.) and Artory (a pioneer in art tokenization), we combine deep industry expertise with technologies like AI and blockchain to modernize a $2.9 trillion global asset class.

• Meaningful equity and competitive compensation
• High‑impact role at a growing company with revenue, funding, and a compelling vision
• Build at the intersection of art, fintech, AI, and blockchain
• A collaborative, pragmatic team that values speed, clarity, and technical quality
• Remote‑flexible culture with an HQ in NYC
• Backed by top VCs and trusted by leading collectors, advisors, and institutions

US Remote or Hybrid (East Coast or Central time‑zone required). For candidates in NY or Miami, an interview may be conducted in person.

We’re hiring a Staff Data Engineer to be the founding technical leader of our data team. You’ll build the data platform from the ground up—the engine that powers WAG’s AI‑driven valuations, market analytics, and collector intelligence. As the first dedicated data engineering hire, you’ll own the entire data architecture—from ingestion and scraping infrastructure to enrichment pipelines, data warehousing, and the datasets that feed our machine learning models and public indices.

This is a team‑founding, tech lead role
: you’ll lay the technical foundation, establish data engineering standards and culture, hire your team, and scale the data platform alongside the company.

You’ll report directly to our Head of Engineering
, a highly hands‑on technical leader who you’ll partner closely with on data architecture decisions and technical strategy. Together, you’ll co‑own the data platform vision
—balancing immediate pipeline needs with long‑term scalability, quality, and governance. Our Head of Engineering is deeply involved in code, design reviews, and technical discussions, so you’ll have a close working relationship focused on building world‑class data systems.

You’ll also work closely with our CPO (Chief Product Officer), domain experts, and company leadership to turn fragmented, messy real‑world data into a durable competitive advantage. This isn’t just about building pipelines—it’s about making architectural decisions on storage, processing, and quality; evaluating tradeoffs between speed and rigor; and building a data platform that can evolve from early‑stage to enterprise‑scale.

This is an AI‑native environment. We move fast using tools like Cursor and Claude Code, build with LLM APIs from OpenAI and others, and actively leverage AI in our product and development workflows. If you’re excited about being a founding technical leader who ships production data systems with AI as a core tool, you’ll thrive here.

• Build the data team from scratch—define the hiring roadmap, recruit and onboard your first 2–3 data engineers, and establish the team’s culture, standards, and ways of working
• Own the entire data platform architecture from day one—make the critical decisions on storage layers, processing frameworks, orchestration, and data modeling patterns
• Define technical standards and best practices for data quality, testing, documentation, lineage, and governance
• Lead system design for complex problems involving large‑scale ingestion, entity resolution, LLM‑powered data extraction, and real‑time analytics
• Evaluate and adopt new technologies that improve data velocity, quality, reliability, or capabilities
  
  Establish data governance frameworks including versioning, reproducibility, validation, and compliance

• Design and operate scalable data ingestion and web scraping systems, including best practices around retries, proxies, rate limiting, and anti‑bot strategies
• Build batch and real‑time pipelines to normalize, enrich, deduplicate, and version data across structured and unstructured sources
• Architect systems to support LLM‑ and ML‑based document parsing, OCR, entity extraction, and…